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TEMPO Ozone OSSE and O ff-line 3DVAR NOx emission constraints

TEMPO Ozone OSSE and O ff-line 3DVAR NOx emission constraints Brad Pierce 1 , Vijay Natraj 2 , Allen Lenzen 1 , Susan Kulawik 3 , Helen Worden 4 , Xiong Liu 5 , Mike Newchurch 6. 1- Space Science and Engineering Center, University of Wisconsin-Madison

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TEMPO Ozone OSSE and O ff-line 3DVAR NOx emission constraints

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  1. TEMPO Ozone OSSE and Off-line 3DVAR NOx emission constraints Brad Pierce1, Vijay Natraj2, Allen Lenzen1, Susan Kulawik3, Helen Worden4, XiongLiu5, Mike Newchurch6 1- Space Science and Engineering Center, University of Wisconsin-Madison 2- Jet Propulsion Laboratory, California Institute of Technology 3- Bay Area Environmental Research Institute 4- National Center for Atmospheric Research 5- Harvard-Smithsonian Center for Astrophysics 6- University of Alabama, Huntsville

  2. TEMPO Ozone OSSE • Assess the impact of assimilating hourly, geostationary UV, UV-VIS, and UV-VIS-TIR ozone profile retrievals within a realistic OSSE environment • Utilize independent modeling systems for generation of Nature atmosphere and conducting assimilation impact experiments • Account for realistic atmospheric variability, which requires evaluation of the nature runs with respect to observations • Include realistic variability in the synthetic radiances, which requires using realistic albedos and emissivities • Include realistic sensitivities, which requires generation of averaging kernels (AK) for each retrieval for use in assimilation studies

  3. Flow Chart

  4. Data Assimilation WRF-CHEM/GSI (3D-VAR) 12km Regional O3 OSSE Study – July 2011 • Control • Synthetic OMI (using retrieval efficiency factors and apriori) • Multiple Regression GEOCAPE UV-VIS-TIR synthetic retrievals • Multiple Regression GEOCAPE UV-VIS synthetic retrievals • Multiple Regression GEOCAPE UV synthetic retrievals • All GEOCAPE experiments include: • 1 hour cycling • Inflation of background error covariances near surface • Application of tangent linear observation operator (AK) in GSI enter loop • Results compared to nature run integrated over atmospheric layers and at AIRNow surface sites

  5. Impact of Assimilation: sfc-6km Results

  6. Impact of UVVIS Assimilation: Urban Sites ASSIM Control Nature July 2011 Mean Diurnal Cycle UVVIS assimilation increases correlation by 5%, reduces bias by 7%, and reduces rms error by 6% at Urban sites

  7. Impact of UVVISTIR Assimilation: Urban Sites ASSIM Control Nature July 2011 Mean Diurnal Cycle UVVISTIR assimilation increases correlation by 9%, reduces bias by 50%, and reduces rms error by 14% at Urban sites

  8. NO2 OSSE Flow Chart VLIDORT (high spectral resolution VIS 400-490nm) Full Optimal Estimation Retrievals (VIS 400-490nm) GOES Sounder Cloud Cleared Retrievals (VIS) WRF-Chem Analysis (NO2, P) WRF-Chem First Guess (NO2, P)

  9. Tropospheric NO2 Column Results Optimal Estimation Column NO2 Retrievals (VIS 400-490nm) 20Z July 18, 2011

  10. Data Assimilation WRF-CHEM/GSI (3D-VAR) 12km Regional NO2 OSSE Study – July 2011 • Control • Synthetic OMI (using retrieval efficiency factors and apriori) • Synthetic TEMPO (using OE averaging kernels and apriori) • NO2 analysis increments are used for off-line adjustments of WRF-Chem NOx emissions

  11. WRF-Chem/GSI NOx emission adjustment experiments • Calculate monthly mean NO2 Jacobian (β) from a 15% NOX emission reduction perturbation experiment following Lamsal et al. 2011 • Calculate monthly mean NO2 analysis increment using WRF-Chem/GSI OMI NO2 assimilation • Adjust 2011 NEI NOx emissions using Jacobian and analysis increment Lamsal, L. N., et al. (2011), Application of satellite observations for timely updates to global anthropogenic NOxemission inventories, Geophys. Res. Lett., 38, L05810, doi:10.1029/2010GL046476.

  12. WRF-Chem OMI Based NOx Emission Adjustment OMI based emission adjustment is 2-4% of NOx emissions (consistent with assimilation of actual OMI NO2)

  13. WRF-Chem TEMPO Based NOx Emission Adjustment TEMPO based emission adjustment is 20-40% of NOx emissions Note change in scale!

  14. WRF-ChemTEMPO NO2 DASurface Ozone response Adjusted Emissions Control Localized 20-30 ppbv changes in surface ozone due to changes in NOx emissions 21Z July 04, 2011

  15. OSSE Impact of TEMPO NOx emissionAdjustment: O3 Urban AirNowSites ASSIM Control Nature No significant change in diurnal behavior of surface ozone

  16. Conclusions • The Ozone OSSE demonstrates systematic and significant increase in lower to mid tropospheric correlations and reductions in rms errors and biases when hourly geostationary UV, UVVIS, and UVVISTIR ozone retrievals are assimilated, respectively. • Comparisons at US EPA surface monitoring sites shows that the overall positive impacts obtained with UVVISTIR retrieval assimilation relative to UVVIS are due to reductions in nighttime biases. • The NO2 OSSE demonstrates significant adjustments in apriori NOx emissions compared to OMI NO2 emission adjustments • Results show low surface ozone sensitivity to changes NOx Point and Area source emissions, possibly due to high urban NOx levels leading to VOC sensitive ozone production

  17. Extra Slides

  18. TropOMITropospheric NO2 column Data Assimilation within NWS NAM-CMAQ NOx emissions adjustments (DE) are constrained using OMI tropospheric NO2 column analysis increments (DW) b accounts for the sensitivity of the NO2 column to changes in NOx emissions following Lamsal et al 2011. Lamsal, L. N., et al. (2011), Application of satellite observations for timely updates to global anthropogenic NOxemission inventories, Geophys. Res. Lett., 38, L05810, doi:10.1029/2010GL046476.

  19. NAM-CMAQ surface ozone response to TropOMI offline NOx Tropospheric NO2 column Data Assimilation

  20. Impact of TropOMI NO2 Offline emission adjustment: Urban Sites

  21. Impact of TropOMI NO2 Offline emission adjustment: Urban Sites

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